Complex Adaptive Systems at the Interface of Ecology and Evolution

Ulf DieckmannEvolution and Ecology ProgramInternational Institute for Applied Systems AnalysisLaxenburg, Austria

Dynamics of Living Systems

Ecology

Changes innumbers

Evolution

Changes inheritable features

Two Common Misperceptions

Evolution is always slowOn the contrary, rapid contemporary evolution is widespread, in particular in response to anthropogenic environmental change

Evolution is always optimizingOn the contrary, frequency-dependent selection is ubiquitous, implying that population-level features will rarely get optimized by evolution

Eco-Evolutionary Models

Derive species- or ecosystem-level predictions from individual-level processes

Forecast rapid evolution

Identify evolutionary mechanisms

Explain ecological structures

Ecosystem

Levels of Complexity

CommunityAnthropogenic

impacts:HarvestingPollution

EutrophicationClimate change

…

Stakeholders

Population

IndividualDem

ogra

phy &

Ada

ptat

ion Regulation & Selection

Three Research Vignettes

Fisheries-induced Evolution

Stakeholder Reconciliation

Community Assembly

Fisheries-induced Evolution

Evolutionary responses of stocks to modern fishing pressures are inevitable

Significant evolution can occur within just a few generations

Evolutionary changes are not necessarily beneficial

Evolutionary changes will often be difficult to reverse

The Overlooked Evolutionary Dimension

Which Traits Are at Risk?

Age and size at maturation Reproducing late is impossible

Reproductive effort Saving for future seasons is futile

Growth rate Staying below mesh size prolongs life

Morphology and behavior Avoiding fishing gear is advantageous

Overview of Case Studies 1/3

American plaice

Bluegill

Argentine

Atlantic silversideAtlantic herring Atlantic salmon

Atlantic cod

Brook troutBanded spiny lobster

Overview of Case Studies 2/3

Coho salmon

Largemouth bass Mozambique tilapia

Common whitefish

Guppy Haddock

Common carp

Grayling

Lake whitefish

Overview of Case Studies 3/3

Witch flounder Yellowtail flounder

Plaice

Small yellow croaker

Sole

Pink salmon Rainbow trout

Sockeye salmonRed porgy

© Google Earth

Feeding groundsBarents Sea,mature & juvenile fishSpawning groundsNorwegian coast,only mature fish

With a catch of 400,000 tonnes per year, Northeast Arctic cod is one of the most important European fish stocks

Northeast Arctic Cod: Stock Structure

Northeast Arctic Cod: Fishing History

Fishing along the Norwegian coast has been intensive for centuries

Trawling in the Barents Sea started in the 1920s and reached its current high level around 1960

This devalues the later parts of anindividual’s lifespan

Evolution of earlier maturationat smaller size is thus expected

A certain combination of age and size at maturation can be observed if:1. Fish survive until that

age and size2. Fish grow to that

age and size3. Fish mature at that

age and size

Maturation Schedules (PMRNs)

Age

Size

Environmentalvariation

in growthand survival

Maturation probability

10%

50%

90%

Leng

th at

50%

mat

urat

ion

prob

abilit

y at a

ge 7

(cm

)

1930 1970 200570

100

90

80

This shift in maturation schedule contributes to a drop in maturation age from 9-10 years to 6-7 years and reduces initial egg production by about 50%

Until about 1970

Today

Northeast Arctic Cod: Evolutionary Change

Eco-Genetic Models

Eco-genetic models are process-based and designed to incorporate salient

Ecological detailtogether with

Genetic detailin the context of apopulation’s fulllife cycle

Northeast Arctic Cod: Model Details

Demography Linear growth before maturation Growth increments of mature

individuals depend on size and gonadosomatic index

Growth increments correlate negatively with stock biomass

Growth increments vary between individuals

Constant natural mortality Density-dependent newborn

mortality Density-dependent cannibalism on

age classes 1 and 2 (optional) Linear probabilistic maturation

reaction norm of constant width Fecundity depends allometrically on

size

Fishing mortality

00.10.20.30.40.50.60.70.80.9

1

1932

1936

1940

1944

1948

1952

1956

1960

1964

1968

1972

1976

1980

1984

1988

1992

1996

Feeding groundsSpawning grounds

Historical fishing:Ffeeding = 0.05 and Fspawning = 0.2

Current fishing:Ffeeding = 0.4 and Fspawning = 0.3

Size selectivity of fishing gear taken into account

Northeast Arctic Cod: Implementation

0 100Time (years)

Currentfishing

Age a

t mat

urat

ion

(yea

rs)

12

10

8

6

4

2

0

Historicalfishing

Eco-genetic model of Northeast Arctic cod

Fast Pace of Evolutionary Decline

ca. 40 yearsTo

day

Age a

t mat

urat

ion

(yea

rs)

Historicalfishing

0 100Time (years)

12

10

8

6

4

2

0

Currentfishing

Eco-genetic model of Northeast Arctic cod

Slow Pace of Evolutionary Recovery

Toda

y

ca. 250 years

Evolutionary Impact Assessment

Stakeholder Reconciliation

0 Population crash

EmploymentYieldProfit

Zoneof new

consensus

Zone of traditional fisheries

management

Fishing effort

Bene

fits (

utilit

y)

Ecosystem preservation

Hilborn 2007: “Zone of New Consensus”

Integrated Assessment

1. Biological modelNortheast Arctic codBarents sea capelin

2. Socio-economic modelFleet costs, revenues, and effort-employment relationships estimated from the profitability surveys of the Norwegian Fisheries Directorate

3. Stakeholder modelHeterogenous preferences

Yield Employ-ment Profit SSB

Industrial fishers 0.3 0 0.7 0

Artisanal fishers 0.5 0.1 0.1 0.3

Employment-oriented society 0.2 0.5 0 0.3

Profit-oriented society 0.2 0 0.6 0.2

Conservationists 0.1 0.2 0.2 0.5

Stakeholder Preferences

Area of joint satisfaction: Consensus most likely

Stakeholder A Stakeholder B

Mapping the Zone of Consensus

Harvest proportion (%)

Mini

mum

size

(cm

)

Capelin Cod

0 20 40 60 80 100 0 20 40 60 80 100

50

100

150

5

1

0

1

5

20

Status quo

70%

90%

Mapping the Zone of Consensus

Community Assembly

Generalized Modeling

Assessing Food-Web Stability

Classical approach: Study linear food-web dynamics and randomize formal parameters (May 1972)

Better approach: Study nonlinear food-web dynamics, calculate equilibrium, linearize around equilibrium, and randomize ecological parameters

Even better approach: Study non-linear food-web dynamics, avoid costly equilibrium calculation, linearize around unknown equilibrium, ecologically interpret resulting parameters, and randomize these (generalized modeling; Gross and Feudel 2006)

New Power Law

Explains99.64% of variation Connectance:

C = LN (N – 1)

First Structure-Stability Rule

Correlation of stability with number ofpredator species preying on focal species

Trophic position of focal prey

Food-web stability is enhanced when

Intermediate prey are utilized by

many predators

Second Structure-Stability Rule

Correlation of stability with number ofprey species predated upon by focal species

Trophic position of focal predator

Food-web stability is enhanced when

Top predators utilize many prey

Shared Trait Spaces

Example: Bivariate Shared Trait Space

Unimodal carrying capacity

Strength of competition attenuates with trait difference

Two traits, of relevance for community dynamics and biological invasions

Random Assembly

Fitness

Bright colors: positive fitness; dark colors: negative fitness

Gradual Evolution

Fitness

Bright colors: positive fitness; dark colors: negative fitness

Gradual Evolution with Radiation

Fitness

Bright colors: positive fitness; dark colors: negative fitness

Evolutionary Food-Web AssemblyBo

dy si

ze

Time

Trophic position

Coregonid Diversification along a Gradient

Lake Stechlin Berlin, Germany

Coregonid Diversification along a Gradient

Evolutionary Ecology Vegetation ModelAverage leaf-area index

Evolutionary Ecology Vegetation Model

Perspectives

The field of theoretical evolutionary ecology is undergoing exciting developments. Some key research frontiers:

Rapid anthropogenic evolution Derivation of fitness from its ecological

underpinnings Calibrated and process-based eco-evolutionary

models Functional traits and ecosystem dynamics